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首页> 外文期刊>Soil Dynamics and Earthquake Engineering >Shear wave velocity and soil type microzonation using neural networks and geographic information system
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Shear wave velocity and soil type microzonation using neural networks and geographic information system

机译：基于神经网络和地理信息系统的剪切波速度和土壤类型微区划

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摘要

Frequent casualties and massive infrastructure damages are strong indicators of the need for dynamic site characterization and systematic evaluation of a site's sustainability against hazards. Microzonation is one of the most popular techniques in assessing a site's hazard potential. Improving conventional macrozonation maps and generating detailed microzonation is a crucial step towards preparedness for hazardous events and their mitigation. In most geoscience studies, the direct measurement of parameters imposes a huge cost on projects. On one hand, field tests are expensive, time-consuming, and require specific high-level expertise. Laboratory methods, on the other hand, are faced with difficulties in perfect sampling. These limitations foster the need for the development of new numerical techniques that correlate simple-accessible data with parameters that can be used as inputs for site characterization. In this paper, a microzonation algorithm that combines neural networks (NNs) and geographic information system (GIS) is developed. In the field, standard penetration and downhole tests are conducted. Atterberg limit test and sieve analysis are performed on soil specimens retrieved during field-testing. The field and laboratory data are used as inputs, in the integrated NNs-GIS algorithm, for developing the microzonation of shear wave velocity and soil type of a selected site. The algorithm is equipped with the ability to automatically update the microzonation maps upon addition of new data.
机译：频繁的人员伤亡和大量的基础设施破坏是需要动态表征场所和系统评估场所可持续性抵御危害的有力指标。微区划是评估站点潜在危害的最流行技术之一。改善常规的宏观分区图并生成详细的微观分区是朝着防范和缓解危险事件迈出的关键一步。在大多数地球科学研究中，直接测量参数会给项目带来巨大的成本。一方面，现场测试昂贵，费时，并且需要特定的高级专家。另一方面，实验室方法在完美采样方面面临困难。这些局限性促使人们需要开发新的数字技术，该技术将简单易访问的数据与可用作现场表征输入的参数相关联。本文提出了一种结合了神经网络和地理信息系统的微区划算法。在现场，进行标准的渗透和井下测试。 Atterberg极限测试和筛分分析是针对在田间测试中获得的土壤样本进行的。在集成的NNs-GIS算法中，现场数据和实验室数据用作输入，用于开发剪切波速度的微分区和选定地点的土壤类型。该算法具备在添加新数据后自动更新微区划图的功能。

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